A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors

With increasingly prominent environmental problems, controlling automobile exhaust has become essential to the environment. The fuel consumption of transportation is the critical factor that determines exhaust gas. By analyzing the naturalistic driving data of heavy-duty diesel trucks (HDDTs), this paper explored the influence of engine technical state, road features, weather, and temperature conditions on fuel consumption during driving. The detailed process is as follows: Firstly, we collected 1153 naturalistic driving data from 34 HDDTs and made a specific analysis and summary description of the data; secondly, by establishing a binary Logistic regression model, we quantitatively explored the influence of significant factors on the fuel consumption; meanwhile, based on quantitative analysis of factor’s effectiveness, this research used several machine learning algorithms (back-propagation neural network, decision tree, and random forest) to build fuel consumption predictors, and compared the prediction performance of different algorithms. The results showed that the prediction accuracy of the decision tree, back-propagation (BP) neural network, and random forest is 81.38%, 83.98%, and 86.58%, respectively. The random forest showed the best performance in predicting. The conclusions can assist transportation companies in formulating driving training strategies and contribute to reducing energy consumption and emissions.

Novel Mathematical Method to Obtain the Optimum Speed and Fuel Reduction in Heavy Diesel Trucks

In Mexico and many parts of the world, land cargo transport units (UTTC) operate at high speeds, causing accidents, increased fuel costs, and high levels of polluting emissions in the atmosphere. The speed in road driving, by the carriers, has been a factor little studied; however, it causes serious damage. This problem is reflected in accidents, road damage, low efficiency in the life of the engine and tires, low fuel efficiency, and high polluting emissions, among others. The official Mexican standard NOM-012-SCT-2-2017 on the weight and maximum dimensions with which motor transport vehicles can circulate, which travel through the general communication routes of the federal jurisdiction, establishes the speed limit at the one to be driven by an operator. Because of the new reality, the uses and customs of truck operators have been affected, mainly in their operating expenses. In this work, a mathematical model is presented with which the optimum driving speed of a UTTC is obtained. The speed is obtained employing the equality between the forces required to move the motor unit and the force that the tractor has available. The required forces considered are the force on the slope, the aerodynamic force, and the friction force, and the force available was considered the engine torque. This mathematical method was tested in seven routes in Mexico, obtaining significant savings of fuel above 10%. However, the best performance route possesses 65% flat terrain and 35% hillocks without mountainous terrain, regular type of highway, and a load of 20,000 kg, where the savings increase up to 16.44%.

Comparative Analysis of Energy Use and Greenhouse Gas Emission of Diesel and Electric Trucks for Food Distribution in Gowanus District of New York City

New York City’s food distribution system is among the largest in the United States. Food is transported by trucks from twelve major distribution centers to the city’s point-of-sale locations. Trucks consume large amounts of energy and contribute to large amounts of greenhouse gas emissions. Therefore, there is interest to increase the efficiency of New York City’s food distribution system. The Gowanus district in New York City is undergoing rezoning from an industrial zone to a mix residential and industrial zone. It serves as a living lab to test new initiatives, policies, and new infrastructure for electric vehicles. We analyze the impact of electrification of food-distribution trucks on greenhouse gas emissions and electricity demand in this paper. However, such analysis faces the challenges of accessing available and granular data, modeling of demands and deliveries that incorporate logistics and inventory management of different types of food retail stores, delivery route selection, and delivery schedule to optimize food distribution. We propose a framework to estimate truck routes for food delivery at a district level. We model the schedule of food delivery from a distribution center to retail stores as a vehicle routing problem using an optimization solver. Our case study shows that diesel trucks consume 300% more energy than electric trucks and generate 40% more greenhouse gases than diesel trucks for food distribution in the Gowanus district.

Emission Characteristics of Hazardous Air Pollutants from Medium-Duty Diesel Trucks Based on Driving Cycles

Studies on the characteristics of hazardous air pollutants (HAPs) in the emissions of medium-duty diesel trucks are significantly insufficient compared to those on heavy-duty trucks. This study investigated the characteristics of regulated pollutants and HAPs, such as volatile organic compounds (VOCs), aldehydes, and polycyclic aromatic hydrocarbons (PAHs), and estimated non-methane hydrocarbon (NMHC) speciation in the emissions of medium-duty diesel trucks. Ten medium-duty diesel trucks conforming to Euros 5 and 6 were tested for four various driving cycles (WLTC, NEDC, CVS-75, and NIER-9) using a chassis dynamometer. In an urban area such as Seoul, CO and NMHC emissions were increased because of its longer low-speed driving time. NOx emissions were the highest in the high-speed phase owing to the influence of thermal NOx. PM emissions were almost not emitted because of the DPF installation. Alkanes dominated non-methane volatile compound (NMVOC) emissions, 36–63% of which resulted from the low reaction of the diesel oxidation catalyst. Formaldehyde emissions were the highest for 35–53% among aldehydes irrespective of driving cycles. By sampling the particle-phase of PAHs, we detected benzo(k)fluoranthene and benzo(a)pyrene and estimated the concentrations of the gas-phase PAHs with models to obtain the total PAH concentrations. In the particle portion, benzo(k)fluoranthene and benzo(a)pyrene were over 69% and over 91%, respectively. The toxic equivalency quantities of benzo(k)fluoranthene and benzo(a)pyrene from NIER-9 (cold) for both Euro 5 and Euro 6 vehicles were more than five times higher than those of NIER (hot) and NEDC. In the case of NMHC speciation, formaldehyde emissions were the highest for 10–45% in all the driving cycles. Formaldehyde and benzene must be controlled in the emissions of medium-duty diesel trucks to reduce their health threats. The results of this study will aid in establishing a national emission inventory system for HAPs of mobile sources in Korea.

Evaluation of the energy consumption of container diesel trucks in a container terminal: A case study at Klaipeda port

Introduction: In the paper, we examine the energy consumption efficiency of specialized container diesel trucks engaged in container transportation at a seaport terminal. Objectives: Using the container terminal at Klaipėda in Lithuania as the background for the research, we produced an improved energy consumption model for measuring the theoretical energy consumption and regeneration of diesel trucks at the terminal and provide a comparative analysis. Methods: We created a mathematical model which describes the instantaneous energy consumption of the diesel trucks, taking into account their dynamic properties and the overall geometry of their routes—“Ship-Truck-Stack-Ship”—using the superposition principle. We investigated other critical parameters relevant to the model and provide a statistical evaluation of the transportation process using data from a case study of Klaipėda port, where we collected measurements of container transportation parameters using georeferenced movement detection and logs from wireless equipment positioned on the diesel-powered container trucks. Results: The modeling results showed that an instantaneous evaluation of energy consumption can reveal areas in the container transportation process which have the highest energy loss and require the introduction of new management and process control initiatives to address the regulations which are designed to decrease harmful industrial emissions and encourage novel technologies and thereby increase the eco-friendliness of existing systems. Conclusion: Based on the research results, the article can provide a reference for the estimation of diesel truck efficiency in seaport terminal operations.